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masridigital

Apollo.io MCP Server

by masridigital

enrich_person

Enrich contact data by providing email, name with company domain, or LinkedIn URL to retrieve professional details including email, phone, social profiles, and employment information.

Instructions

Enrich a person's data with email, phone, social profiles, employment info, and more. Provide either email, name+domain, or LinkedIn URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailNoPerson's email address
first_nameNoFirst name (use with domain)
last_nameNoLast name (use with domain)
domainNoCompany domain (e.g., apollo.io)
linkedin_urlNoLinkedIn profile URL
reveal_personal_emailsNoInclude personal email addresses

Implementation Reference

  • The main execution logic for the 'enrich_person' tool. Calls Apollo's /people/match endpoint with input args and formats the enriched person data into a readable text response.
    private async enrichPerson(args: any) {
      const response = await this.axiosInstance.post("/people/match", args);
      const person = response.data.person;
    
      if (!person) {
        return {
          content: [
            {
              type: "text",
              text: "No person found with the provided information.",
            },
          ],
        };
      }
    
      let result = `Person Enrichment Results:\n\n`;
      result += `Name: ${person.first_name} ${person.last_name}\n`;
      result += `ID: ${person.id}\n`;
      result += `Title: ${person.title || "N/A"}\n`;
      result += `Email: ${person.email || "N/A"}\n`;
      result += `Phone: ${person.phone_numbers?.[0]?.raw_number || "N/A"}\n`;
      result += `LinkedIn: ${person.linkedin_url || "N/A"}\n`;
      result += `Location: ${person.city ? `${person.city}, ${person.state || ""}` : "N/A"}\n`;
      result += `Seniority: ${person.seniority || "N/A"}\n\n`;
    
      if (person.organization) {
        result += `Company Information:\n`;
        result += `  Name: ${person.organization.name}\n`;
        result += `  Domain: ${person.organization.website_url || "N/A"}\n`;
        result += `  Industry: ${person.organization.industry || "N/A"}\n`;
        result += `  Employees: ${person.organization.estimated_num_employees || "N/A"}\n`;
      }
    
      return {
        content: [
          {
            type: "text",
            text: result,
          },
        ],
      };
    }
  • The tool registration in getTools() including name, description, and detailed inputSchema defining parameters for person enrichment.
    {
      name: "enrich_person",
      description:
        "Enrich a person's data with email, phone, social profiles, employment info, and more. Provide either email, name+domain, or LinkedIn URL.",
      inputSchema: {
        type: "object",
        properties: {
          email: {
            type: "string",
            description: "Person's email address",
          },
          first_name: {
            type: "string",
            description: "First name (use with domain)",
          },
          last_name: {
            type: "string",
            description: "Last name (use with domain)",
          },
          domain: {
            type: "string",
            description: "Company domain (e.g., apollo.io)",
          },
          linkedin_url: {
            type: "string",
            description: "LinkedIn profile URL",
          },
          reveal_personal_emails: {
            type: "boolean",
            description: "Include personal email addresses",
          },
        },
      },
    },
  • src/index.ts:66-67 (registration)
    Dispatch/registration case in the CallToolRequestSchema handler that routes 'enrich_person' calls to the enrichPerson method.
    case "enrich_person":
      return await this.enrichPerson(args);
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions what data gets enriched, it doesn't describe behavioral traits such as whether this is a read-only or mutation operation, rate limits, authentication requirements, or what happens if multiple input parameters are provided. For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with two sentences: the first states the purpose and enrichment scope, and the second specifies input requirements. It's front-loaded with the core functionality, and both sentences earn their place by adding value. Minor deduction because it could be slightly more structured (e.g., bullet points for input options).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (enriching person data with multiple input options), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the enriched output looks like, error conditions, or behavioral constraints like rate limits or data sources. For a tool with no structured safety or output information, the description should provide more context to be fully helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all six parameters thoroughly. The description adds marginal value by grouping parameters into logical input combinations ('email, name+domain, or LinkedIn URL'), but it doesn't provide additional semantics beyond what's in the schema, such as explaining interactions between parameters or the purpose of 'reveal_personal_emails' in context. Baseline 3 is appropriate when schema coverage is high.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Enrich a person's data with email, phone, social profiles, employment info, and more.' It specifies the verb ('enrich') and resource ('person's data') with concrete examples of enrichment types. However, it doesn't explicitly distinguish this tool from sibling tools like 'search_people' or 'get_person_activity', which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides some usage guidance by stating 'Provide either email, name+domain, or LinkedIn URL,' which indicates acceptable input combinations. However, it doesn't explicitly state when to use this tool versus alternatives like 'search_people' or 'find_email', nor does it mention any prerequisites or exclusions. The guidance is implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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